Platform Modernization
Data-Modernization Discovery for a Multi-Billion-Dollar Dealer Group
Client: Regional auto-dealership group (60+ rooftops)
Situation
At a multi-billion-dollar auto-dealership group, HQ analysts closed the month in spreadsheets emailed from store controllers, keying numbers by hand for days after close, with no canonical store-level or network roll-up data store.
Task
Run a focused discovery to design the data-engineering function and architecture that would replace the manual month-end close with an automated pipeline and a presentation layer the HQ team could use.
Action
Delivered an onsite discovery week: interviewed HQ analysts against a structured framework, mapped the controller-data ingestion, and architected a Microsoft Fabric lakehouse (raw to canonical store-level to network roll-up) with single sign-on and a custom Next.js analytics layer, plus a phased roadmap toward Claude-powered analysis agents.
Results
- Replaced a hire-a-data-engineer decision with a fractional build plan
- Architecture to automate a multi-day manual month-end close
- Executive-ready roadmap from discovery to a production lakehouse
Transformation
Before: Month-end close run by hand in Excel
Store controllers emailed spreadsheets to HQ, where a dozen analysts keyed numbers for days after close with no canonical data store underneath.
- Spreadsheets emailed from 60+ rooftops
- Days of manual keying after every close
- No canonical store-level or network roll-up
- Weighing an expensive in-house data-engineer hire
After: A lakehouse blueprint and a fractional build plan
A Microsoft Fabric lakehouse design with a canonical model and a custom analytics layer, scoped as a phased build instead of a full-time hire.
- Raw to canonical to network-roll-up architecture
- Custom Next.js analytics layer over the lakehouse
- Phased roadmap toward Claude analysis agents
- Fractional build plan in place of a $200K hire
Key Metrics
Month-end close
Data store
Staffing
Key metrics
Dealership rooftops
61
HQ analysts
12
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